A new parallel algorithm for improving the computational efficiency of multi-GNSS precise orbit determination

Abstract

The computational efficiency is critical with the increasing number of GNSS satellites and ground stations since many unknown parameters must be estimated. Although only active parameters are kept in the normal equation in sequential least square estimation, the computational cost for parameter elimination is still a heavy burden. Therefore, it is necessary to optimize the procedure of parameter elimination to enhance the computational efficiency of GNSS network solutions. An efficient parallel algorithm is developed for accelerating parameter estimation based on modern multi-core processors. In the parallel algorithm, a multi-thread guided scheduling scheme, and cache memory traffic optimizations are implemented in parallelized sub-blocks for normal-equation-level operations. Compared with the traditional serial scheme, the computational time of parameter estimations can be reduced by a factor of three due to the new parallel algorithm using a six-core processor. Our results also confirm that the architecture of computers entirely limits the performance of the parallel algorithm. All the parallel optimizations are also investigated in detail according to the characteristics of CPU architecture. This gives a good reference to architecture-oriented parallel programming in the future development of GNSS software. The performance of the multi-thread parallel algorithm is expected to improve further with the upgrade of new multi-core coprocessors.s

A batch parameter elimination algorithm for improving the efficiency of multi- GNSS integrated orbit determination

“…However, parallelism sacrificed CPU and memory usage for improving POD efficiency, and it confirmed that the architecture of computers entirely limits the performance of the parallel algorithm (Chen et al, 2022). It is worth mentioning that, in 2020, intel released a unified programming model named oneAPI that simplifies the development process of heterogeneous computing in different architectures, and maximizes performance to meet the needs of different workloads.…”

“…Besides, Open Multiprocessing (OpenMP) has emerged to support the multi-platform shared memory multiprocessing programming (Costa et al 2004;Mironov et al 2017), and the OpenMPbased parallelism has been introduced into the extended Kalman filter for real-time GPS network solutions (Kuang et al 2019). For multi-GNSS POD, a new parallel elimination of the inactive parameters was realized for improving the efficiency of multi-GNSS POD (Chen et al, 2022).…”

An Improved Parameter Estimation Method for High-Efficiency Multi-GNSS-Integrated Orbit Determination

“…Moreover, Open Multiprocessing (OpenMP) has emerged to support multi-platform shared memory multiprocessing programming [28,29], and OpenMP-based parallelism has been introduced into the extended Kalman filter for real-time GPS network solutions [30]. For multi-GNSS POD, a new parallel elimination of the inactive parameters was realized for improving the efficiency of multi-GNSS POD [31]. Parallelism is always used to improve real-time data processing efficiency; however, it sacrificed CPU and memory usage, and it was confirmed that the architecture of computers entirely limits the performance of the parallel algorithm [31].…”

“…For multi-GNSS POD, a new parallel elimination of the inactive parameters was realized for improving the efficiency of multi-GNSS POD [31]. Parallelism is always used to improve real-time data processing efficiency; however, it sacrificed CPU and memory usage, and it was confirmed that the architecture of computers entirely limits the performance of the parallel algorithm [31]. Thus, these parallel algorithms are not fully applicable to improve the efficiency of the postprocessing tasks, especially for the completion of the later global navigation system construction of BDS-3 and Galileo [8,32], and about 40~50 newly launched satellites were added to the routine POD.…”